Method for inspecting an object

ABSTRACT

A method for inspecting an object includes receiving or determining inspection image data, the inspection image data including an inspection image pixel array with at least one inspection image pixel in the inspection image pixel array having a pixel property associated therewith. The method includes receiving via a processor a user input associated with a continuous segment of inspection image pixels in the inspection image pixel array. The method includes determining a property of the object based on the pixel properties associated with the continuous segment of inspection image pixels in the inspection image pixel array.

FIELD

The present disclosure relates to a method for inspecting an object,such as a method for inspecting a gas turbine engine part using a visualimage recording device.

BACKGROUND

Known methods of inspecting an object include the use of a visual imagerecording device, such as a camera, to generate one or more inspectionimages and the use of a 3D model of the object. In such a manner, theseknown inspection methods generally require access to the 3D model of theobject in order to complete the inspection.

BRIEF DESCRIPTION OF THE DRAWINGS

A full and enabling disclosure, including the best mode thereof,directed to one of ordinary skill in the art, is set forth in thespecification, which makes reference to the appended figures, in which:

FIG. 1A is a perspective view of a three-dimensional (3D) model of anobject and a virtual visual image recording device in accordance with anexemplary embodiment of the present disclosure.

FIG. 1B is a perspective view of the 3D model of the object and thevirtual visual image recording device of FIG. 1A in accordance with anexemplary embodiment of the present disclosure.

FIG. 1C is a perspective view of the 3D model of the object and thevirtual visual image recording device of FIG. 1A in accordance with anexemplary embodiment of the present disclosure.

FIG. 2 is a portion of a guide image pixel array in accordance with anexemplary embodiment of the present disclosure.

FIG. 3A is a table displaying a plurality of pixel properties for theguide image pixel of FIG. 2 in accordance with an exemplary embodimentof the present disclosure.

FIG. 3B is a table displaying pixel properties for the guide image pixelof FIG. 2 in accordance with an exemplary embodiment of the presentdisclosure.

FIG. 4 is a visual depiction of an algorithm to determine one or morepixel properties for a guide image pixel in accordance with an exemplaryembodiment of the present disclosure.

FIG. 5 is a visual depiction of an algorithm to determine pixelproperties for a guide image pixel in accordance with an exemplaryembodiment of the present disclosure.

FIG. 6 is a visual depiction of an algorithm to determine pixelproperties for a guide image pixel in accordance with an exemplaryembodiment of the present disclosure.

FIG. 7 is a visual depiction of an algorithm to determine pixelproperties for a guide image pixel in accordance with an exemplaryembodiment of the present disclosure.

FIG. 8 is a simplified image depicting the 3D model of the object ofFIG. 1A in accordance with an exemplary embodiment of the presentdisclosure.

FIG. 9 is a flow chart diagram illustrating a method for determiningpixel properties and creating a simplified image of a 3D model inaccordance with an exemplary embodiment of the present disclosure.

FIG. 10 is an inspection image of an object in accordance with anexemplary embodiment of the present disclosure.

FIG. 11 is a simplified image of the inspection image of FIG. 10 inaccordance with an exemplary embodiment of the present disclosure.

FIG. 12 is an inspection image pixel array of the inspection image ofFIG. 10 in accordance with an exemplary embodiment of the presentdisclosure.

FIG. 13 is a method for estimating an orientation of an object inaccordance with an exemplary embodiment of the present disclosure.

FIG. 14 is a flow chart diagram illustrating a method for determining aproperty of an object in accordance with an exemplary embodiment of thepresent disclosure.

FIG. 15 is an inspection image with a portion of the inspection imagepixel array of FIG. 12 designated in accordance with an exemplaryembodiment of the present disclosure.

FIG. 16 is an inspection image with a portion of the inspection imagepixel array of FIG. 12 designated in accordance with an exemplaryembodiment of the present disclosure.

FIG. 17 is an inspection image with a portion of the inspection imagepixel array of FIG. 12 designated in accordance with an exemplaryembodiment of the present disclosure.

FIG. 18 is a flow chart diagram illustrating a method for determining aproperty of an object in accordance with an exemplary embodiment of thepresent disclosure.

FIG. 19 is a flow chart diagram illustrating a method for storing aninspection package of an object in accordance with an exemplaryembodiment of the present disclosure.

FIG. 20 is a flow chart diagram illustrating a method for comparing afirst property of an object with a second property of the object inaccordance with an exemplary embodiment of the present disclosure.

FIG. 21 is a block diagram of a computing system in accordance with anexemplary embodiment of the present disclosure.

FIG. 22 is a block diagram of an inspection system in accordance with anexemplary embodiment of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to present embodiments of thedisclosure, one or more examples of which are illustrated in theaccompanying drawings. The detailed description uses numerical andletter designations to refer to features in the drawings. Like orsimilar designations in the drawings and description have been used torefer to like or similar parts of the disclosure.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any implementation described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other implementations. Additionally, unlessspecifically identified otherwise, all embodiments described hereinshould be considered exemplary.

The terms “coupled,” “fixed,” “attached to,” and the like refer to bothdirect coupling, fixing, or attaching, as well as indirect coupling,fixing, or attaching through one or more intermediate components orfeatures, unless otherwise specified herein.

As used herein, the terms “first”, “second”, and “third” may be usedinterchangeably to distinguish one component from another and are notintended to signify location or importance of the individual components.

The singular forms “a”, “an”, and “the” include plural references unlessthe context clearly dictates otherwise.

Approximating language, as used herein throughout the specification andclaims, is applied to modify any quantitative representation that couldpermissibly vary without resulting in a change in the basic function towhich it is related. Accordingly, a value modified by a term or terms,such as “about”, “approximately”, and “substantially”, are not to belimited to the precise value specified. In at least some instances, theapproximating language may correspond to the precision of an instrumentfor measuring the value, or the precision of the methods or machines forconstructing or manufacturing the components and/or systems. Forexample, the approximating language may refer to being within a 1, 2, 4,10, 15, or 20 percent margin. These approximating margins may apply to asingle value, either or both endpoints defining numerical ranges, and/orthe margin for ranges between endpoints.

Here and throughout the specification and claims, range limitations arecombined and interchanged, such ranges are identified and include allthe sub-ranges contained therein unless context or language indicatesotherwise. For example, all ranges disclosed herein are inclusive of theendpoints, and the endpoints are independently combinable with eachother.

The inventors of the present disclosure have found that it may bebeneficial to be able to inspect an object without access to the 3Dmodel of the object. Improved inspection methods would be welcomed inthe field of component inspection.

In accordance with one or more embodiments described herein, a methodfor inspecting an object is provided. The method includes determiningguide image data of the object from a determined orientation, the guideimage data including a guide image pixel array and a pixel property forat least one guide image pixel in the guide image pixel array. Themethod also includes receiving inspection image data indicative of aninspection image. The method also includes associating the inspectionimage data with the guide image data and determining a property of theobject based on the guide image data and the associated inspection imagedata. Determining the property of the object based on the guide imagedata and the associated inspection image data has several benefits.

First, determining the property of the object based on the guide imagedata and the associated inspection image data may make it possible toinspect the object without the use of a 3D model of the object, e.g.,after having determined guide image data for the object. For example,and as will be described in more detail, guide image data can bedetermined from the 3D model for a determined orientation of the object.Once the guide image data is determined, the 3D model is no longerneeded to determine the property of the object. As such, if guide imagedata is determined, the object can be inspected without using, viewing,or sharing the 3D model of the object.

Inspecting the object without using, viewing, or sharing the 3D model ofthe object has several benefits. For example, the information within the3D model may be proprietary information that is undesirable to discloseto third parties, such as a user that is inspecting the object. Also,certain countries may have export compliance laws that may prohibit theexport of the 3D model outside of the country without an export license.However, it may be acceptable to share guide image data, which isinformation related to the 3D model of the object.

Second, determining the property of the object based on the guide imagedata and the associated inspection image data can reduce the amount oftime to perform an inspection of an object. For example, knowninspection methods involve overlaying inspection images onto 3D modelsand manually matching features between the inspection images and the 3Dmodels. Such overlaying, comparison, and matching directly to the 3Dmodel may be very time consuming. A such, determining the property ofthe object based on the guide image data and the associated inspectionimage data can reduce the amount of time to perform an inspection.

In accordance with one or more embodiments described herein, a methodfor inspecting an object is provided. The method includes determininginspection image data, the inspection image data including an inspectionimage pixel array with each inspection image pixel in the inspectionimage pixel array having a pixel property associated therewith. Themethod also includes receiving a user input associated with a continuoussegment of inspection image pixels in the inspection image pixel array.The method also includes determining a property of the object based onthe pixel properties associated with the continuous segment ofinspection image pixels in the inspection image pixel array. Determiningthe property of the object based on the pixel properties associated withthe received user input associated with the continuous segment ofinspection image pixels in the inspection image pixel array has manybenefits.

First, determining the property of the object based on the pixelproperties associated with the received user input associated with thecontinuous segment of inspection image pixels in the inspection imagepixel array may have the benefit of the ability to compare a property ofthe object to a threshold value. For example, the received user inputmay be associated with a feature on an object. In some examples, thefeature can be damage to the object, such as a crack or chip on theobject or a spalling location of a coating on the object. As such, itmay be beneficial to measure the feature to determine whether it iswithin an acceptable range. For example, cracks under a certain lengthmay be acceptable. However, cracks over a certain length may require theobject to be repaired or replaced.

Second, determining the property of the object based on the pixelproperties associated with the received user input associated with thecontinuous segment of inspection image pixels in the inspection imagepixel array may have the benefit of the ability to track and compare thelocation or measurement of certain features, such as damage to theobject, between different objects. For example, the object may be arotor blade and the user input may be associated with a crack on therotor blade. The determined property of the object may be the length ofthe crack and the location of the crack on the rotor blade. In one ormore other inspection events to inspect other rotor blades, the userinput may also be associated with cracks on the rotor blades and thedetermined property of the object may also be the length of the crackand the location of the crack on the rotor blade. The crack size andlocation of cracks on different rotor blades can be analyzed todetermine trends. For example, an analysis may be performed thatdetermines that the most likely location for a crack on a rotor blade.

In accordance with one or more embodiments described herein, a methodfor inspecting an object is provided. The method includes recalling afirst inspection package that includes a first inspection image of theobject and a first designation. The method also includes receiving dataindicative of a second inspection package that includes a secondinspection image of the object and a second designation. The method alsoincludes receiving, recalling, or both, one or more properties maps ofthe object. The method also includes determining a first property of theobject based on the first inspection image of the object, the one ormore properties maps of the object, and the first designation. Themethod also includes determining a second property of the object basedon the second inspection image of the object, the one or more propertiesmaps of the object, and the second designation. The method also includesdisplaying the first property and the second property and/or comparingthe first property with the second property.

In certain aspects, the method includes receiving or recalling a firstproperties map and receiving or recalling a second properties map. Insuch a case, the method may determine the first property of the objectbased on the first properties map and similarly may determine the secondproperty of the object based on the second properties map.

In certain aspects, the method includes receiving or recalling a secondproperties map. In such a case, the method may determine the firstproperty of the object based on the second properties map and similarlymay determine the second property of the object based on the secondproperties map.

The method also includes displaying or comparing the first property withthe second property. Displaying or comparing the first property with thesecond property has several benefits.

First, displaying or comparing the first property with the secondproperty may make it possible to inspect the object without the use of a3D model of the object. For example, the first property is determinedbased on the first or second properties map of the object, and thesecond property is similarly determined based on the first or secondproperties map of the object. The first properties map and the secondproperties map can be determined from the 3D model for a determinedorientation of the object. Once the first and/or second properties mapsare determined, the 3D model is no longer needed to determine the firstproperty or the second property of the object.

Inspecting the object without using, viewing, or sharing the 3D model ofthe object has several benefits. For example, the information within the3D model may be proprietary information that is undesirable to discloseto third parties, such as a user that is inspecting the object. Also,certain countries may have export compliance laws that may prohibit theexport of the 3D model outside of the country without an export license.However, it may be acceptable to share pixel properties, which isinformation related to the 3D model of the object.

Second, displaying or comparing the first property with the secondproperty may reduce the amount of time to perform an inspection of anobject. For example, known inspection methods involve overlayinginspection images onto 3D models and manually matching features betweenthe inspection images and the 3D models. Such overlaying, comparison,and matching directly to the 3D model may be very time consuming. Assuch, because the first property and the second property are determinedbased on the first properties map of the object or the second propertiesmap of the object, and not the 3D model, comparing the first propertywith the second property may reduce the amount of time to perform aninspection.

Third, displaying or comparing the first property with the secondproperty may allow the measurement of a feature on an object over time.In some examples, the feature can be damage to the object, such as acrack or chip on the object or a spalling location of a coating on theobject. As such, it may be beneficial to measure the size of the featureto determine whether it is within an acceptable range. For example,cracks under a certain length may be acceptable. Additionally, it may bebeneficial to measure the feature at two different points in time, suchas two different points in time that are at least three months apart andup to twenty years apart, to determine whether the feature is growingand project when the feature will grow to exceed an acceptable size. Inyet another example, the feature, such as a crack, can be measured andcompared on different objects so that a trend analysis can be performed.For example, it may be useful to analyze the data to determine the mostlikely location of a crack or the most likely size of a crack.

Fourth, in some examples, determining the first property of the objectand the second property of the object are both based on the secondproperties map of the object. This may ensure an apples-to-applescomparison of the first property with the second property. This may beimportant when an algorithm to determine the first properties mapdiffers from an algorithm to determine the second properties map.Because algorithms may change over time due to optimizations orimprovements to the algorithm, the first properties map may differ fromthe second properties map. Because the first properties map may differfrom the second properties map, it may be beneficial to redetermine thefirst property with the second properties map of the object (using thefirst inspection image and first designation) to ensure anapples-to-apples comparison to the second property. Notably, in thisscenario, the 3D model is not needed.

Referring to the drawings, wherein identical numerals indicate the sameelements throughout the figures, FIG. 1A provides a perspective view ofa three-dimensional (3D) model 200 (also referred to as a “computermodel”) of an object 100 and a virtual visual image recording device 230in accordance with an exemplary embodiment of the present disclosure. Asused herein, an “object” can be a component, a portion of a component,an assembly that includes a plurality of components, or a portion of anassembly. Even though shown as a black and white drawing in the figures,it should be understood that the 3D model 200 may be a shaded 3D model,such as a 3D model 200 with a grayscale color scheme, to provideadditional visual details, such as the curvatures of the component. Inone example, the 3D model 200 can be created with the use of 3D modelingsoftware. For example, the 3D model 200 can be created withcomputer-aided design (CAD) software. In another example, the 3D model200 can be created by 3D scanning a real-world object, such as a controlobject. A control object can be a specimen with known dimensions and/orqualities. The virtual visual image recording device 230 can representany suitable imaging device including any optical sensor capable ofcapturing still or moving images, such as a camera. Suitable types ofcameras may be a CMOS camera, a CCD camera, a digital camera, a videocamera or any other type of device capable of capturing an image. It isfurther contemplated that a borescope camera or an endoscope camera canbe utilized. Further still, the visual image recording device may be amonocular camera or a binocular camera.

The 3D object 100 may be positioned in a “pose” in front of the virtualvisual image recording device 230. More specifically, the 3D object 100may be positioned in a determined orientation. As used herein,“orientation” refers to the physical position of an object 100. Forexample, “orientation” can refer to the relative position of one object100 relative to another object 100. For example, as used in reference toFIG. 1A, the determined orientation refers to the relative physicalposition of the 3D object 100 relative to the virtual visual imagerecording device 230. Alternatively, or in addition, “orientation” canrefer to the six degrees of freedom positioning—position on the X-axis(surge), position on the Y-axis (sway), position on the Z-axis (heave),tilt on the X-axis (roll), tilt on the Y-axis (pitch), and tilt on theZ-axis (yaw).

Referring to FIG. 1B, a perspective view of the 3D model 200 of theobject 100 and the virtual visual image recording device 230 of FIG. 1Ais provided in accordance with an exemplary embodiment of the presentdisclosure. As shown, a plurality of rays 235 may be cast from alocation associated with the virtual visual image recording device 230.Even though a virtual visual image recording device 230 is depicted, itshould be understood that a virtual visual image recording device 230 isnot required. For example, the rays 235 could be cast from a specificpoint 232 that is in a particular location relative to the object 100.

Referring to FIG. 1C, a perspective view of the 3D model 200 of theobject 100 and the virtual visual image recording device 230 of FIG. 1Ais provided in accordance with an exemplary embodiment of the presentdisclosure. The plurality of rays 235 (FIG. 1B) that are cast onto theobject 100 create an array of guide image pixels 251, a guide imagepixel array 250. Each of the guide image pixels 251 are subdivisions ofthe surface of the object 100 that are created by the rays 235. Itshould be understood that the size of each of the guide image pixels 251are exaggerated for clarity in the figures. For example, the guide imagepixels 251 can range in size from about 0.05 millimeter to about 5millimeters.

As seen in this view, at least some, and sometimes all, of the guideimage pixels 251 may be irregular shapes such that they have sides andangles of any shape and size. Also, the shape and size of the sides andangles may differ among the guide image pixels 251 of the guide imagepixel array 250. For example, the shape of each of the guide imagepixels 251 may be dependent on the location and shape of the surface ofthe object 100 that the corresponding rays 235 are cast upon.

Referring to FIG. 2 , a portion of a guide image pixel array 250 isprovided in accordance with an exemplary embodiment of the presentdisclosure. The guide image pixel array 250 can include a plurality ofguide image pixels 251. Even though only nine guide image pixels 251 areshown in this view, the guide image pixel array 250 can include anynumber of guide image pixels 251. For example, the guide image pixelarray 250 can include more than five hundred thousand guide image pixels251, such as more than one million guide image pixels 251, such as morethan two million guide image pixels 251, and up to one hundred millionguide image pixels 251. The number of guide image pixels 251 within theguide image pixel array 250 may be dependent on a variety of factorssuch as user preferences, computing power, screen resolution, size ofthe object 100, cast location of the rays 235, and/or the number of rays235.

Each guide image pixel 251 within a guide image pixel array 250 can haveanother guide image pixel 251 that is adjacent to it, which is anadjacent guide image pixel 253. An adjacent guide image pixel 253 is aguide image pixel 251 that shares a common side with another guide imagepixel 251. For example, in the example of FIG. 2 , guide image pixel251′, which is guide image pixel B2 (column, row location), has eightadjacent guide image pixels 253: A1, A2, A3, B1, B3, C1, C2, and C3(column, row location).

Referring to FIG. 3A, a table 260 displaying a plurality of pixelproperties 252 for guide image pixel 251′ of FIG. 2 is provided inaccordance with an exemplary embodiment of the present disclosure. Inparticular, for the embodiment of FIG. 3A, the table 260 is a pluralityof pixel properties 252 for guide image pixel 251′ at location B2 inFIG. 2 . One or more pixel properties 252 for at least one of the guideimage pixels 251 within the guide image pixel array 250 can bedetermined. In some examples, one or more pixel properties 252 for eachof the guide image pixels 251 within the guide image pixel array 250 canbe determined. A “properties map”, as used herein, refers to the one ormore pixel properties 252 for the at least one of the guide image pixels251 within the guide image pixel array 250 for an object 100. The pixelproperty 252 can be determined by, for example, deciding, calculating,estimating, or measuring. As will be explained in more detail, a pixelproperty 252 can be information that relates to the guide image pixel251. For example, a pixel property 252 can be a dimension, a distance, alocation, a feature, a color, a surface inclination, a categorization, athreshold value, a criticality, etc. When the pixel property 252 is adistance, the distance can be a Euclidian or a Geodesic distance. Whenthe pixel property 252 is a distance or a dimension, the measurement,calculation, or estimation can account for the topology of the 3D object100. The pixel property 252 can be a numerical value, a text value, abinary value, a quantitative value, a qualitative value, a categoricalvalue, a symbolic value, or a combination thereof, to name a fewexamples. In some examples, a pixel property 252 for a guide image pixel251 may not be available or exist. When a pixel property 252 is notavailable or does not exist, the pixel property 252 may be representedas a ‘0’ or as ‘Nan’, for example. While it is described herein that apixel property is determined for each of the guide image pixels 251 itwill be understood that this need not be the case. Pixel properties canbe determined for any set of the guide image pixels 251 including only asingle guide image pixel. Further still it will be understood that pixelproperties can be determined for a subset of the guide image pixels andthat such guide image pixels need not be adjacent.

In this example, the pixel properties 252 are a set of distances 263.The set of distances can be the distance from a guide image pixel 251 toadjacent guide image pixels 253. The distance 263 can determined bycalculation, estimation, or measurement. In this example, the distance263 is from the center of guide image pixel 251′ at location B2 to thecenter of each adjacent guide image pixel 253 (pixels at locationsA1-A3, B1, B3, and C1-C3). As such, in the embodiment of FIG. 3A, thepixel properties 252 for guide image pixel 251′ at location B2 includeseight distances.

Referring to FIG. 3B, a table 260 displaying additional pixel properties252 for guide image pixel 251′ at location B2 of FIG. 2 is provided inaccordance with an exemplary embodiment of the present disclosure. Inthis example, the object 100 is a rotor blade for a gas turbine engineand the pixel properties 252 for guide image pixel 251′ can includeother information or data. The area, surface inclination, and surfacecolor of guide image pixel 251′ can be included in the pixel properties252. The angle and length of the ray 235 to the center of the guideimage pixel 251′ can be included in the pixel properties 252.Additionally, information regarding whether guide image pixel 251′includes a cooling hole of the rotor blade, and the location of theguide image pixel 251′ can be included in the pixel properties 252. Forexample, when the object 100 is a rotor blade for a gas turbine engine,the location can be categorized as a leading edge, a trailing edge anairfoil, a platform, etc. Other calculations, estimations, ormeasurements can also be included in the pixel properties 252. Forexample, distances from various locations of guide image pixel 251′ toother various locations of guide image pixel 251′ are included in thepixel properties 252, in this example. Even though only a few examplesof data that can be included in the guide image pixel properties 252have been provided, it should be understood that other data can beincluded in the guide image pixel properties 252. For example, the angleof each of the corners, the angle of the ray 235 to each of the corners,the angle of the ray 235 to the middle of each side, the distance 263from each of the corners, etc.

Referring to FIG. 4 , a visual depiction of an algorithm 259 todetermine one or more pixel properties 252 for a guide image pixel 251is provided in accordance with an exemplary embodiment of the presentdisclosure. As used herein, “algorithm” refers to a set of instructionsto achieve a particular goal. For example, there could be various waysto achieve the goal of determining distances between one guide imagepixel 251 and another guide image pixel 251. The algorithm to achievethe goal of determining distances between guide image pixels 251 may bea specific set of instructions on how to determine the distances betweenguide image pixels 251.

Referring still to FIG. 4 , a pixel property 252 can be a set ofdistances 263, which can be determined, as mentioned. For example, theset of distances 263 can be the distance 263 from a guide image pixel251 to its adjacent guide image pixels 253. In the example of FIG. 4 , adistance 263 is calculated, estimated, or measured from the center ofthe guide image pixel 251 to each of the adjacent guide image pixels253. The distances, represented as the straight line that goes from thecenter of the guide image pixel 251 to the center of the adjacent guideimage pixel 253, may be stored as a numerical value in a table 260, suchas the tables 260 of FIG. 3A or FIG. 3B.

Referring to FIG. 5 , a visual depiction of an algorithm 259 todetermine pixel properties 252 for a guide image pixel 251 is providedin accordance with an exemplary embodiment of the present disclosure.More specifically, an algorithm 259 to determine a set of distances 263is provided in accordance with an exemplary embodiment of the presentdisclosure. In this example, a distance 263 may be calculated,estimated, or measured from the center of the guide image pixel 251 tothe closest region of the adjacent guide image pixel 253. This can berepeated for each adjacent guide image pixel 253 to determine a set ofdistances 263. The distance 263, represented as the straight line thatgoes from the center of the guide image pixel 251 to the closest regionof the adjacent guide image pixels 253, may be stored as a numericalvalue in a table 260, such as the tables of FIG. 3A or FIG. 3B.

Referring to FIG. 6 , a visual depiction of an algorithm 259 todetermine pixel properties 252 for a guide image pixel 251 is providedin accordance with an exemplary embodiment of the present disclosure.More specifically, an algorithm 259 to determine a set of distances 263is provided in accordance with an exemplary embodiment of the presentdisclosure. In this example, a distance 263 may be calculated,estimated, or measured from a first adjacent guide image pixel 253 a toa second adjacent guide image pixel 253 b of the guide image pixel 251by making a straight-line from a middle of a side or a corner of a firstadjacent guide image pixel 253 a, through the guide image pixel 251, andto a middle of a side or a corner of a second adjacent guide image pixel253 b. This can be repeated for each adjacent guide image pixel 253 todetermine a set of distances. The pixel properties 252, which are thedistances 263 represented as the straight lines that traverse throughthe guide image pixels 251, may be stored as numerical values in a table260, such as the tables of FIG. 3A or FIG. 3B.

Referring to FIG. 7 , a visual depiction of an algorithm 259 todetermine pixel properties 252 for a guide image pixel 251 is providedin accordance with an exemplary embodiment of the present disclosure.More specifically, an algorithm 259 to determine a set of distances 263is provided in accordance with an exemplary embodiment of the presentdisclosure. In this example, a distance 263 may be calculated,estimated, or measured from a first adjacent guide image pixel 253 a toa second adjacent guide image pixel 253 b of the guide image pixel 251by making the shortest, straight-line path from the first adjacent guideimage pixel 253 a to the center of the guide image pixel 251 and thenthe shortest, straight-line path from the center of the guide imagepixel 251 to the second adjacent guide image pixel 253 b. This can berepeated for each adjacent guide image pixel 253 to determine a set ofdistances. The pixel properties 252, which are the distances 263represented as the straight lines that traverse through the guide imagepixels 251, may be stored as numerical values in a table 260, such asthe tables of FIG. 3A or FIG. 3B.

As will be appreciated further from the discussion herein below, one ormore of the above algorithms 259 may be used to determine a distance orarea on an inspection image in response to a user input selectionassociated with a continuous segment of inspection image pixels.Notably, however, in order to utilize the pixel properties 252 of theguide image pixels 251 of the guide image, the inspection image may needto be associated with the guide image to facilitate use of the pixelproperties 252 of the guide image pixels 251 in response to the userinput. Such aspects are described in more detail below.

Referring now to FIG. 8 , a simplified image 210 depicting the 3D model200 of the object 100 of FIG. 1A is provided in accordance with anexemplary embodiment of the present disclosure. As shown, the simplifiedimage 210 may be a two-dimensional (2D) line drawing of the 3D model 200in the determined orientation. In other examples, the simplified image210 is a 2D image that realistically depicts the 3D model 200. Forexample, the simplified image 210 may be a 2D grayscale image or a 2Dcolored image. In at least one example, the simplified image 210 is animage that is made to appear to be a photograph of the object. Forexample, the simplified image 210 may have the same or similarappearance, style, color, shading, etc. as a photograph of the object.The simplified image 210 may depict only key features 220. Key features220 may be the most relevant and/or prominent features. For example, andreferring to the example of FIG. 8 , which provides a simplified image210 of the 3D model 200 of the rotor blade of FIG. 1A, the simplifiedimage 210 may include lines that represent the outside perimeter 220 a,the shape of the tip 220 b, the shape of the blade platform 220 c, andthe dovetail root end 220 d. However, other features can be represented,such as cooling holes. As will be explained in more detail, creating asimplified image 210 can assist in determining an orientation of anobject 100 and correlating pixel properties 252.

Referring to FIG. 9 , a method 300 for determining pixel properties 252and creating a simplified image 210 of a 3D model 200 is provided inaccordance with an exemplary embodiment of the present disclosure. Themethod 300 can include a step 310 of creating a 3D model 200 of anobject 100 in a determined orientation. The method 300 can include astep 320 of creating a guide image pixel array 250. The method 300 caninclude a step 330 of determining a pixel property 252 for at least oneguide image pixel 251 in the guide image pixel array 250 (a propertiesmap). Properties can include but are not limited to, dimensionalproperties, area properties, depth information, etc. The method 300 caninclude a step 340 of creating a simplified image 210 of the 3D model200 in the determined orientation. In some examples, method 300 does notinclude step 340. As will be explained further, steps of other methodscan be performed with the 3D model in lieu of the simplified image 210of the 3D model 200. Also, the timing of step 340 is not affected by thetiming of when the other steps of method 300 are performed.

Method 300 can be repeated numerous times. For example, it may bebeneficial to perform method 300 for various determined orientations ofthe 3D model 200. For example, method 300 can be repeated up totwo-hundred times, for example, fifty to one-hundred fifty times, eachfor a different determined orientation of the 3D model 200. Therefore,for each determined orientation of the 3D model 200, one or more pixelproperties 252 for at least one guide image pixel 251 in the guide imagepixel array 250 can be determined. Also, for each determined orientationof the 3D model 200, a simplified image 210 of the 3D model 200 of theobject 100 can be created in the determined orientation. As will beexplained later, the determined orientation can be associated with the3D model 200, a simplified image 210 of the 3D model 200, or aninspection image.

The determined orientation of each of the various determined orientationmay vary slightly. For example, the various determined orientations mayvary by up to 2 mm on the X-axis, Y-axis, and/or Z-axis and/or within 2degrees for tilt on the X-axis, Y-axis, and Z-axis.

Referring to FIG. 10 , an inspection image 400 of an object 100 isprovided in accordance with an exemplary embodiment of the presentdisclosure. The inspection image 400 may be a picture of an object 100,which in this example is a real-world object. The inspection image canbe produced with a visual image recording device such as the visualimage recording device 230 previously described. Inspection image dataindicative of the inspection image may be generated. Inspection imagedata may include the inspection image 400, the capture date (the date ordate and time the inspection image 400 was generated), the number ofcycles, the number of hours in operation, the location where theinspection image 400 was generated, an identification of the object 100under inspection, an identification of the user that was operating thevisual image recording device when the inspection image 400 wasgenerated, the positioning of the visual image recording device when theinspection image 400 was generated, etc.

The visual image recording device may be a component of an inspectiontool assembly. The inspection tool assembly can assist the operator withtaking photographs at a desired orientation. Therefore, the picture,such as the picture of FIG. 10 , can be taken at a desired orientation.However, variations of the orientation of the image in relation to thevisual image recording device may exist due to various factors. Thevarious factors may be tolerances, such as component tolerances,assembly tolerances, and installation tolerances, and/or mechanical wearin the inspection tool assembly. As such, the actual orientation of theobject 100 in relation to the visual image recording device may deviatefrom the desired orientation.

In some examples, a “stream” of inspection images 400, such as picturesof a real-world object, can be created. For example, when the real-worldobject is a rotor blade for a gas turbine engine, the rotor system onwhich the rotor blade is installed can be rotated while a series ofpictures are taken with, e.g., a borescope camera, creating the streamof pictures. In some instances, the inspection tool assembly may includea mounting structure configured to mount the borescope camera to the gasturbine engine and through a borescope opening (or other opening) tofacilitate the borescope camera taking pictures of the components (e.g.,the rotor blades), from a consistent position and orientation betweenmultiple inspections of the same engine, as well as from a consistentposition and orientation between multiple inspections of differentengines of the same or similar model.

As will be explained in greater detail, the picture that most closelymatches the orientation of a 3D model 200 or a simplified image 210 of a3D model 200 can be selected for further analysis.

In some examples, the picture can be corrected. For example, the picturemay be corrected for any optical distortions to account for lens andmanufacturing variation for the visual image recording device and/or forthe inspection tool assembly. Additionally, or alternatively, thepicture can be corrected to account for relatively minor inconsistenciesin the orientation of the object in the picture relative to theorientation of the 3D model 200 or the simplified image 210 of a 3Dmodel 200.

Referring to FIG. 11 , a simplified image 410 of the inspection image400 of FIG. 10 is provided in accordance with an exemplary embodiment ofthe present disclosure. In this example, the simplified image 410 isdisplayed as a line drawing and the object 100 is a rotor blade for agas turbine engine. In other examples, the simplified image 410 of theobject 100 may be a 2D grayscale image or a 2D colored image. Forexample, the simplified image 410 can be a photograph of the object 100.

The simplified image 410 may depict key features 420. Key features 420may be the most relevant and/or prominent features. For example, the keyfeatures 420 may be the outside perimeter 420 a of the simplified image,the shape of the tip 420 b, the blade platform 420 c, or the abutment420 d of the rotor blade with an adjacent rotor blade. However, otherfeatures can be represented, such as cooling holes in a non-limitingexample.

Filters, such as a Canny edge detection filter or a Sobel filter may beapplied to the picture to assist in extracting the key features 420.However, other machine learning, such as deep learning-basedsegmentation techniques, can be used to provide a learned representationof the features that are most prominent.

Referring to FIG. 12 , an inspection image pixel array 450 of theinspection image 400 of FIG. 10 is provided in accordance with anexemplary embodiment of the present disclosure. As shown, the inspectionimage 400 of the object 100 can be subdivided into inspection imagepixels 451, creating an inspection image pixel array 450. In anotherexample, the simplified image 410 of the object 100 can be subdividedinto an array of inspection image pixels 451, forming an inspectionimage pixel array 450. It should be understood that the size of each ofthe inspection image pixels 451 are exaggerated for clarity in thefigures. Each inspection image pixel 451 can be any size and there canbe any number of inspection image pixels 451 within the inspection imagepixel array 450. The number of inspection image pixels 451 and/or thesize of each of the inspection image pixels 451 may be dependent on thesize and/or resolution of the inspection image 400. As will be explainedin more detail, the inspection image pixel array 450 and/or inspectionimage pixels 451 can be associated with a guide image pixel array 250and/or guide image pixels 251.

Referring to FIG. 13 , a method 500 for estimating an orientation of anobject 100 is provided in accordance with an exemplary embodiment of thepresent disclosure. The method 500 can include a step 510 of obtainingan inspection image 400 of an object 100, such as the object 100 of FIG.10 . The method 500 can include a step 520 of creating a simplifiedimage 410 of the inspection image 400, such as the simplified image 410of FIG. 11 . The method 500 can include a step 530 of estimating anorientation of the object 100. In at least one example, the method 500includes steps 510, 520, and 530. In other examples, the method 500 doesnot include step 520, but includes steps 510 and 530.

The orientation of the object 100 may be estimated by inputting theinspection image 400 or the simplified image 410 of the object 100 intoa pose recovery regression, which may be a machine learning network,such as a deep learning network. This network can be trained withsynthetic data and can estimate the orientation of the real-world objectbased on the 2D inspection image 400 of the object 100 and/or thesimplified image 410 of the inspection image 400. The synthetic data maybe a plurality of training images with known extrinsic data. Forexample, the synthetic data may be a plurality of training images thathave known, determined orientations. The training images may besimplified images 210, which can be 2D images that realistically depictthe 3D model 200 or a 2D image that only shows key features 220 of the3D model 200. In some examples, the training images are images that aremade to appear to be inspection images 400. For example, the trainingimages may have the same or similar appearance, style, color, shading,etc. as inspection images 400.

The orientation of the object 100 may be estimated by “matching” theinspection image 400 of the object 100 or the simplified image 410 ofthe object 100 to a simplified image 210 of a 3D model 200. For example,as mentioned, numerous simplified images 210 of a 3D model 200 can becreated, each depicting the 3D model 200 in a different, determinedorientation. The simplified image 410 or the inspection image 400 can becompared to one or more of the simplified images 210 of the 3D model200. The simplified image 210 of the 3D model 200 that best matches thesimplified image 210 of the 3D model 200 can be chosen, either by ahuman or by a computing system. The orientation of the object 100 canthen be estimated to be the same as, or similar to, the determinedorientation of the 3D model 200 of the chosen simplified image. Forexample, the estimated orientation of the object 100 can be estimated tobe within a certain tolerance of the determined orientation of the 3Dmodel 200 of the chosen simplified image. The certain tolerance couldbe, for example, within 3 mm on the X-axis, Y-axis, and Z-axis andwithin 5 degrees for tilt on the X-axis, Y-axis, and Z-axis. When thedetermined orientation of the 3D model 200 is based on a 3D scan of acontrol object, additional tolerances could be accounted for due topotential tolerances inherent to the 3D scanning machine and/ortolerances of the control object.

In at least one example, the inspection image 400 or the simplifiedimage 410 of the inspection image 400 may be transformed. The inspectionimage 400 or the simplified image 410 of the inspection image 400 may betransformed to correct for residual differences between the simplifiedimage 210 of the 3D model 200 and the inspection image 400 or thesimplified image 410 of the inspection image 400. Transforms can includelinear and rotational offset, scale, shear, pinch/punch, distortion,tombstone correction, etc. The transforms may decrease the differencesbetween the simplified image 410 of the inspection image 400 and thesimplified image 210 of the 3D model 200, or may decrease thedifferences between the inspection image 400 and the simplified image210 of the 3D model. The transformations can occur before or after beingmatched to the simplified image 210 of the 3D model 200.

Transform data may be generated to document the amount and location oftransforms that were made to reduce the residual differences between thesimplified image 210 of the 3D model 200 and the inspection image 400 orthe simplified image 410 of the inspection image 400. For example,transform data may be quantitative values and/or qualitative values thatdescribe the transform. The transform data can include six degree offreedom orientation transforms. Transform data may include informationindicative of an inspection image 400 relative to the determinedorientation associated with the guide image data of the object 100. Forexample, the transform data may include information indicative of thesix degrees of freedom orientation corrections made to reduce thedifferences between the simplified image 210 of the 3D model 200 and theinspection image 400 or the simplified image 410 of the inspection image400.

Generating transform data has several benefits. For example, theinspection image 400 or a subsequent inspection image 400, such as asecond inspection image 400, may be transformed using the transform dataat a later date or time. Also, guide image data, can be transformedusing the transform data.

Pixel properties 252 that are created from a 3D model 200 of an object100 can be associated with the inspection image 400. The pixelproperties 252 can be associated based on the inspection image 400,transformed or non-transformed, the simplified image 410 of theinspection image 400, transformed or non-transformed, the estimatedorientation of the inspection image 400, or a combination thereof.

For example, and as earlier explained, an orientation of an inspectionimage 400 can be determined by matching the inspection image 400 of anobject 100 or the simplified image 410 of an object 100 to a simplifiedimage 210 of a 3D model 200. The matched simplified image 210 of the 3Dmodel 200 can have an associated guide image pixel array 250, each ofthe guide image pixels 251 having one or more pixel properties 252. Theguide image pixel array 250, along with their pixel properties 252, canbe associated with the inspection image 400 of the object 100 or thesimplified image 410 of the object 100. More specifically, at least oneof the guide image pixels 251 of a simplified image 210 of a 3D model200 can be associated with at least one inspection image pixel 451 of aninspection image 400 of an object 100 or a simplified image 410 of anobject 100.

In some examples, there is a 1:1 ratio of association between guideimage pixels 251 and inspection image pixels 451. However, in anotherexample, there is an uneven ratio such that it is not a 1:1 ratio. Forexample, more than one guide image pixel 251 can be associated with oneinspection image pixel 451. In another example, more than one inspectionimage pixel 451 can be associated with one inspection image pixel 451.An uneven ratio may be caused by higher resolutions or lower resolutionsof the visual image recording device used to generate the inspectionimage 450 as compared to the resolution of the simplified image 210 ofthe 3D model 200.

There are various ways of associating guide image pixels 251 of asimplified image 210 of a 3D model 200 with an inspection image 400 ofan object 100 or a simplified image 410 of an object 100. For example,the guide image pixels 251 can be “mapped” to corresponding inspectionimage pixels 451. For example, X, Y coordinates for the boundaries ofeach of the guide image pixels 251 of the simplified image 210 of the 3Dmodel 200 can be generated. X, Y coordinates for the boundaries of eachof the inspection image pixels 451 of the inspection image 400 can begenerated. Because the inspection image 400, or the simplified image410, can be “matched” to a simplified image 210 of the 3D model 200, theX, Y coordinates of the inspection image 400 can be matched to the X,Ycoordinates of the simplified image 210 of the 3D model 200. In effect,the inspection image 400 can be “overlayed” with the guide image pixels251 of the simplified image 210 of the 3D model 200. Therefore, each ofthe guide image pixels 251 can be mapped, or associated with, acorresponding inspection image pixel 451.

It should be noted that an inspection image pixel 451 can be of adifferent size than the associated guide image pixel 251. For example,the guide image pixel 251 may be larger than the inspection image pixel451, relative to the object 100. As such, two or more inspection imagepixels 451 may be associated with a guide image pixel 251.

In another example, the guide image pixels 251 of the simplified image210 of the 3D model 200 are “transferred” onto the inspection image 400.Information regarding the boundaries of each of the guide image pixels251 can be stored within the inspection image 400. For example, theinformation regarding the boundaries of each of the guide image pixels251 can be stored as metadata of the inspection image 400.

In yet another example, a table 260 correlating the X, Y coordinates ofthe inspection image 400 with the corresponding guide image pixel 251 ofthe simplified image 210 of the 3D model 200 can be created.

Once guide image pixels 251 of a simplified image 210 of a 3D model 200are associated with an image of an object 100 or a simplified image 210of an object 100, the pixel properties 252 of each of the guide imagepixels 251 can also be associated with the image of the object 100 orthe simplified image 210 of the object 100. For example, because eachguide image pixel 251 can have corresponding pixel properties 252, theguide image pixels 251 along with their pixel properties 252 can beassociated with the image of the object 100 or the simplified image 210of the object 100.

Referring to FIG. 14 , a method 600 for determining a property of anobject 100 is provided in accordance with an exemplary embodiment of thepresent disclosure. The method 600 includes a step 610 of determiningguide image data of an object 100 from a determined orientation. Guideimage data can include a guide image pixel array 250 and a pixelproperty 252 for at least one guide image pixel 251 in the guide imagepixel array 250 (a properties map). Guide image data may also include asimplified image 210 of the 3D model 200 of the object 100. As earlierexplained, the guide image pixels 251, the pixel properties 252, and thesimplified image 210 of the 3D model 200 of the object 100 can becreated from a 3D model 200 of an object 100.

The method 600 includes a step 620 of receiving or determininginspection image data indicative of an inspection image 400. Theinspection image data can include an inspection image 400 of the object100, a simplified image 410 of the object 100, an estimated orientationof the object 100, an inspection image pixel array 450, or a combinationthereof.

The method 600 includes a step 630 of associating the inspection imagedata with the guide image data. As explained, there are various ways ofassociating guide image pixels 251 of a simplified image 210 of a 3Dmodel 200 with an image of an object 100 or a simplified image 410 of anobject 100.

The method 600 includes a step 640 of determining a property of theobject 100 based on the guide image data and the associated inspectionimage data. As explained, once guide image pixels 251 of a simplifiedimage 210 of a 3D model 200 are associated with an image of an object100 or a simplified image 410 of an object 100, the pixel properties 252of each of the guide image pixels 251 can also be associated with theimage of the object 100 or the simplified image 410 of an object 100.For example, the pixel properties 252 of each of the guide image pixels251 in a guide image pixel array can be associated with a correspondinginspection image pixel 451 in an array of inspection image pixels 451.

More specifically, one or more properties of the object 100 can bedetermined based on guide image data and the associated inspection imagedata for each inspection image pixel 451 in an inspection image pixelarray 450. As such, one or more properties of the object 100 can bedetermined. The one or more properties of the object 100 can be anyinformation including, but not limited to, dimension, a distance, alocation, a feature, a color, a surface inclination, a categorization, aquality, a criticality, etc. When the property is a distance, thedistance can be a Euclidian or a Geodesic distance. When the property isa distance or a dimension, the measurement, calculation, or estimationcan account for the topology of the object. The property can be anumerical value, a text value, a binary value, a quantitative value, aqualitative value, a categorical value, a symbolic value, or acombination thereof, to name a few examples. Other calculations,estimations, or measurements can also be a property of the object.

Referring to FIG. 15 , an inspection image 400 with a portion 700 of theinspection image pixel array 450 of FIG. 12 designated is provided inaccordance with an exemplary embodiment of the present disclosure. Inthis example, seven of the inspection image pixels 451 of the inspectionimage pixel array 450 have been designated. The inspection image pixels451 can be designated by a user. For example, a user could designate oneor more inspection image pixels 451 with a peripheral device. Theperipheral device could be a touch screen, a stylus, a mouse, arollerball, etc.

The inspection image 400 and the inspection image pixel array 450 can bedisplayed to the user through a peripheral device. The peripheral devicemay be an auxiliary device of a computing system that can display animage to a user. For example, the peripheral device may be a computermonitor and may be a touch screen monitor.

The user could designate a continuous segment 710 of inspection imagepixels 451 of the inspection image pixel array 450. As used herein, a“continuous segment” refers to a plurality of inspection image pixels451 within an inspection image pixel array 450 that are designated andadjacent to at least one other designated inspection image pixel. Asused herein, “designation” refers to the continuous segment 710 ofinspection image pixels 451 of the inspection image pixel array 450 thatis designated by a user or a computing system. For example, a user coulddesignate one or more inspection image pixels 451 by drawing a line 720,as shown. The line 720 drawn by the user may also be displayed to theuser through the peripheral device. The user could draw a straight line,a curved line, a line having no specific geometry or profile, or acombination thereof. The inspection image pixels 451 that the line 720passes through can be designated, creating a linear, continuous segment710 of inspection image pixels. The linear, continuous segment 710 ofinspection pixels could represent a curved or straight line 720. Inregions that the line 720 is perpendicular to or parallel to the X-axis,the line 720 can designate a portion of the inspection image pixel array450 that is one pixel wide or one pixel tall. In regions that the line720 is oblique to or acute to the X-axis, the line 720 can designate aportion of the inspection image 400 that is at least two pixels wide orat least two pixels tall or one pixel wide and one pixel tall. Forexample, in regions that the line 720 is at a 30 degree angle to theX-axis, the line 720 can designate a portion that is one pixel tall andfive pixels wide. As another example, in regions that the line 720 is ata forty-five degree angle to the X-axis, the line 720 can designate aportion that is one pixel wide and one pixel tall such that the cornersof the pixels within the portion have corners that are adjoined.Anti-aliasing methods may be used to account for sub-pixel interpolationof user designations. For example, anti-aliasing methods can be used toimprove the appearance of the continuous segment 710 of inspection imagepixels 451 of the inspection image pixel array 450.

In another example, a computing system could designate a continuoussegment 710 of inspection image pixels 451 of the inspection image pixelarray 450. For example, the computing system could be taught, throughmachine learning, to analyze the inspection image 400 to identifycertain key features. These key features may be automaticallydesignated.

The user could designate a continuous, closed segment 710′ of inspectionimage pixels 451. For example, one or more inspection image pixels 451could be designated by drawing a line 720′ that encompasses, partiallyor fully, an area of the inspection image 400. The area of theinspection image 400 that is encompassed, partially or fully, by theline 720′ can be automatically designated, creating a closed segment710′ of inspection image pixels 451.

Referring to FIG. 16 , an inspection image 400 with a portion 700 of theinspection image pixel array 450 of FIG. 12 designated is provided inaccordance with an exemplary embodiment of the present disclosure. Theinspection image pixels 451 can be designated the same or similarly asthe example embodiment of FIG. 15 However, in this example, theinspection image pixel array 450 is not displayed to the user.

Referring to FIG. 17 , an inspection image 400 with a portion 700 of theinspection image pixel array 450 of FIG. 12 designated is provided inaccordance with an exemplary embodiment of the present disclosure. Theinspection image pixels 451 can be designated the same or similarly asthe example embodiment of FIG. 15 . However, in this example, neitherthe inspection image pixel array 450 nor the designated portions 700 ofthe inspection image pixel array 450 is displayed to the user.

Referring to FIG. 18 , a method 800 for determining a property of anobject 100 is provided in accordance with an exemplary embodiment of thepresent disclosure. Method 800 can include one or more of the steps ofmethod 300, as discussed above. For example, method 800 can include thestep 310 of creating a 3D model 200 of an object 100 in a determinedorientation. The method 800 can include the step 320 of creating a guideimage pixel array 250. The method 800 can include the step 330 ofdetermining a pixel property 252 for at least one guide image pixel 251in the guide image pixel array 250. The method 800 can include the step340 of creating a simplified image 410 of the 3D model 200 in thedetermined orientation.

Steps 310, 320, 330, and 340 can be repeated numerous times. Forexample, it may be beneficial to perform the steps for variousdetermined orientations of the 3D model 200. For example, method 300 canbe repeated up to ten-thousand times, such as up to five-thousand times,such as up to one-thousand times, such as up to two-hundred times, eachfor a different determined orientation of the 3D model 200. Therefore,for each determined orientation of the 3D model 200, one or more pixelproperties 252 for at least one guide image pixel 251 in the guide imagepixel array 250 can be determined. Also, for each determined orientationof the 3D model 200, a simplified image 210 of the 3D model 200 of theobject 100 can be created in the determined orientation. The determinedorientation of each of the various determined orientation may vary onlyslightly. For example, the various determined orientations may only varyby only up to 2 mm on the X-axis, Y-axis, and/or Z-axis and/or within 2degrees for tilt on the X-axis, Y-axis, and Z-axis.

Method 800 can include one or more of the steps of method 500, asdiscussed above. For example, method 800 can include the step 510 ofobtaining an inspection image 400 of an object 100, such as the object100 of FIG. 10 . Method 800 can include the step 520 of creating asimplified image 410 of the inspection image 400, such as the simplifiedimage 410 of FIG. 11 . Method 800 can include the step 530 of estimatingan orientation of the object 100. As explained, the orientation of theobject 100 may be estimated by “matching” the image of the object 100 orthe simplified image 410 of an object 100 to a simplified image 210 of a3D model 200.

Method 800 can include one or more of the steps of method 600, asdiscussed above. For example, method 800 can include the step 610 ofdetermining guide image data of an object 100 from a determinedorientation. Guide image data can include a guide image pixel array 250and a pixel property 252 for at least one guide image pixel 251 in theguide image pixel array 250. As earlier explained, the guide imagepixels 251 and the pixel properties 252 can be created from a 3D model200 of an object 100.

Method 800 can include the step 620 of receiving or determininginspection image data indicative of an inspection image 400. Theinspection image data can include an inspection image 400 of the object100, a simplified image 410 of an object 100, an estimated orientationof the object 100, an inspection image pixel array 450, or a combinationthereof.

Method 800 can include the step 630 of associating the inspection imagedata with the guide image data. As explained, there are various ways ofassociating guide image pixels 251 of a simplified image 210 of a 3Dmodel 200 with an image of an object 100 or a simplified image 410 of anobject 100.

Method 800 can include the step 640 of determining a property of theobject 100 based on the guide image data and the associated inspectionimage data. As explained, once guide image pixels 251 of a simplifiedimage 210 of a 3D model 200 are associated with an image of an object100 or a simplified image 410 of an object 100, the pixel properties 252of each of the guide image pixels 251 can also be associated with theimage of the object 100 or the simplified image 410 of an object 100.For example, the pixel properties 252 of each of the guide image pixels251 in a guide image pixel array can be associated with a correspondinginspection image pixel 451 in an inspection image pixel array 450.

Method 800 can include a step 810 of receiving a user input associatedwith a continuous segment of inspection image pixels 451 in theinspection image pixel array 450 (a designation). As explained, a usercould designate one or more inspection image pixels 451 with aperipheral device.

Method 800 can include a step 820 of determining a property of theobject 100 based on the pixel properties 252 associated with thecontinuous segment of inspection image pixels 451 in the inspectionimage pixel array 450. As explained in reference to step 640, one ormore properties of the object 100 can be determined based on guide imagedata and the associated inspection image data for each inspection imagepixel 451 in an inspection image pixel array 450. As such, one or moreproperties of the object 100 can be determined based on the one or moreinspection image pixels 451 designated by the user.

The one or more properties of the object 100 can be any information thatrelates to the continuous segment of inspection image pixels 451. Forexample, the one or more properties could be a dimension, a distance, alocation, a feature, a color, a surface inclination, a categorization, aquality, a criticality, etc. When the property is a distance, thedistance can be a Euclidian or a Geodesic distance. When the property isa distance or a dimension, the measurement, calculation, or estimationcan account for the topology of the object. The property can be anumerical value, a text value, a binary value, a quantitative value, aqualitative value, a categorical value, a symbolic value, or acombination thereof, to name a few examples. Other calculations,estimations, or measurements can also be a property of the object.

In at least one example, a user could designate a continuous segment 710of inspection image pixels 451 that represents a feature of the object.The feature of the object 100 can be any portion of the object 100 thatis of interest to the user. For example, the feature could be a coolinghole location for a gas turbine engine blade. In other examples, thefeature could be damage to the object 100 that may be visible to theuser in the inspection image 400. More specifically, the feature couldbe a crack on the object, a chip on the object, or a spalling locationof a coating on the object.

In at least one example, the user can “trace” a crack on an inspectionimage 400 of an object 100, such as an inspection image 400 of a gasturbine engine, with a line 720. The inspection image pixels 451 thatthe line traverses through can be designated. Therefore, variousinformation and data regarding the crack can be determined. For example,a length of the crack can be determined. Referring briefly to theexamples provided in FIG. 4 through FIG. 7 , a set of distances 263 canbe determined. The distance 263 can be the distance 263 from a guideimage pixel 251 to its adjacent guide image pixels 253. These distances263 can be associated with inspection image pixels 451. In order todetermine a length of a crack, the distances 263 between a designatedpixel and an adjacent designated pixel is determined, for each of thedesignated pixels in a continuous segment. The distances 263 betweeneach of the designated pixels and the adjacent designated pixel is thensummed to determine a total length of the crack.

In at least one example, the user can select a region on an inspectionimage 400 of an object 100, such as an inspection image 400 of a gasturbine engine rotor blade, with a line 720 to designate a closedsegment 710′ of inspection image pixels 451. Therefore, variousinformation and data regarding the region can be determined.

For example, the total area of the region can be determined. Referringbriefly to the example provided in FIG. 3B, a pixel property 252 of aguide image pixel 251 can be the area of the guide image pixel 251. Theguide image pixel property 252 can be associated with an inspectionimage pixel 451. As such, the guide image pixel property 252, such as anarea of a pixel, can be associated with a designated pixel. To determinethe area of the designated closed segment 710′ of inspection imagepixels 451, the areas of each of the designated pixels within the closedsegment 710′ are summed.

Method 800 can include providing an indication to a user correspondingto the property of the object. For example, the property of the object100 based on the guide image data and the associated inspection imagedata can be displayed to the user through a peripheral device of acomputing system, such as a computer monitor. In another example, theproperty of the object 100 based on the pixel properties 252 associatedwith the continuous segment of inspection image pixels 451 in theinspection image pixel array 450 can be displayed to the user through aperipheral device of a computing system, such as a computer monitor. Asanother example, an indication as to whether the property of the objectexceeds a threshold value can be displayed to the user through aperipheral device of the computing system. For example, a first propertyof the object may be the length of a designation, which can correspondto a length of a crack on the object. A second property of the objectmay be a threshold value for the length of the crack on the object. Ifthe length of the crack exceeds the threshold value, an indication thatthe length of the crack exceeds the threshold value can be displayed tothe user. If the length of the crack does not exceed the thresholdvalue, an indication that the length of the crack is within anacceptable tolerance can be displayed to the user.

In yet another example, a first property of the object may be the lengthof a designation, which can correspond to a length of a crack on theobject 100. A second property of the object may be the location of thedesignation, which can correspond to a location of the crack on theobject 100. A third property of the object 100 may be a threshold valuefor a length of a crack on the object at the corresponding location. Ifthe length of the crack exceeds the threshold value for thecorresponding location, an indication that the length of the crackexceeds the threshold value can be displayed to the user. If the lengthof the crack does not exceed the threshold value, an indication that thelength of the crack is within an acceptable tolerance can be displayedto the user. In this way, one or more properties of the object 100, suchas the location and the length of a designation, can be compared againstanother property, such as the threshold value for the length of thecrack at the location of the designation. The results of the comparisoncan be displayed to the user.

In yet another example, a first property of the object 100 may be afirst size of a feature on an object 100, such as a crack, and a secondproperty of the object 100 may be a second size of the feature on theobject 100. The first size of the feature on the object 100 can becompared to the second size of the feature on the object 100 todetermine a growth of the feature on the object based on the first sizeof the feature on the object 100 and the second size of the feature onthe object 100. The growth of the feature on the object 100 can becompared to a threshold value. An indication can be displayed as towhether the growth of the feature on the object 100 exceeds a thresholdvalue.

In addition to providing an indication to a user corresponding to theproperty of the object, other information, such as a prediction as towhen the property of the object 100 will exceed a threshold value couldbe displayed to the user.

Some properties of the object, such as a threshold value, may be storedin locations other than in the properties map. For example, one or moreproperties of the object can be stored in a memory device of a computingsystem.

Method 800 can include initiating a maintenance action of the object 100in response to determining the property of the object 100. For example,if the property of the object 100, such as a size of a crack, chip, orspalling location, exceeds a threshold value, a maintenance action ofthe object 100 can be initiated. Other factors may be considered todetermine whether to initiate a maintenance action of the object 100 inresponse to determining the property of the object 100. For example,when the object 100 is a rotor blade for a gas turbine engine, thelength of time between the next scheduled repair or overhaul event canbe considered. If it is projected that the property of the object 100,such as a length of a crack, may grow to exceed a threshold value beforethe next scheduled repair or overhaul event, a maintenance action of theobject 100 may be initiated. A “maintenance action” can be an inspectionof the object 100 that can be scheduled based on operational parametersof the object 100. For example, when the object 100 is a component for agas turbine engine, the inspection can be based on a thrust limit,temperature limit, time limit, cycles limit, etc. In some examples, a“maintenance action” can be an operational limitation placed on thecomponent. For example, when the object 100 is a component for a gasturbine engine, the object 100 can be derated such that the thrustrating of the object is reduced, which can reduce the thrust powerrating for takeoff and/or climb of the aircraft that the gas turbineengine is installed on. A “maintenance action” can also be a materialordering action (procuring replacement components), an overhaulscheduling action, a preventative maintenance action, or a repairaction.

Referring to FIG. 19 , a method 900 for storing an inspection package ofan object 100 is provided in accordance with an exemplary embodiment ofthe present disclosure. Method 900 can be similar to method 800 and caninclude methods 300, 500, 600. In this example, however, method 900includes a step 830 of storing a first inspection package that includesthe inspection image 400 of the object 100 and the user input associatedwith a continuous segment 710 of inspection image pixels 451 in theinspection image pixel array 450 (the designation). The first inspectionpackage may also include the one or more pixel properties 252 for atleast one guide image pixel 251 in the guide image pixel array 250 usingthe first algorithm (the properties map), and/or the transform data,and/or the determined orientation. The first inspection package can bestored on a computing system.

One of the benefits of storing the inspection package is that the step820 of determining a property of the object 100 based on the pixelproperties associated with the continuous segment 710 of inspectionimage pixels 451 in the inspection image pixel array 450 can beredetermined using a second algorithm, a second properties map, or asecond designation. For example, algorithms may change over time, whichmay also change the properties map, and methods for designations maychange over time, which may also change a property of the object 100. Analgorithm or method for designation may change because of anoptimization or an improvement to the algorithm or method fordesignation. Therefore, it may be useful to redetermine past propertiesof the object 100 based on the newer, second algorithm, the newer,second properties map, or the newer, second method for designation. Thismay ensure an apples-to-apples comparison (comparing using the samealgorithm, properties map, or designation) of an object 100, a propertyof the object 100, or a feature of an object 100 over time, which willbe explained in more detail, even if the algorithm for determining theproperties map changes or if the method for designation changes.

In at least one example, in lieu of, or in addition to, storing theinspection package of the object 100, other inspection data can bestored. For example, inspection data can include the designation, theestimated orientation of the object 100, or the property of the object100 based on the designation.

Referring to FIG. 20 , a method 1000 for comparing a first property ofan object 100 with a second property of the object 100 is provided inaccordance with an exemplary embodiment of the present disclosure.Method 1000 can include a step 1010 of recalling a first inspectionpackage that includes a first inspection image 400 of the object 100 anda first designation. The first inspection image 400 package may alsoinclude a first properties map of the object 100. As mentioned inreference to FIG. 19 and method 900, the first inspection package can bestored on a computing system. Therefore, the first inspection packagecan later be retrieved from the computing system. Method 1000 caninclude a step 1015 of receiving, recalling, or both, one or moreproperties maps of the object 100. Step 1015 may include receiving orrecalling a first properties map and receiving or recalling a secondproperties map.

Method 1000 can include a step 1020 of receiving data indicative of asecond inspection package that includes a second inspection image 400 ofthe object 100 and a second designation. As mentioned in reference tostep 510 of method 500, an inspection image 400 of an object 100 can beobtained. As mentioned in reference to step 810 of method 800, a userinput associated with a continuous segment 710 of inspection imagepixels 451 in the inspection image pixel array 450 (a designation) canbe received.

Step 1020 may also include displaying the first designation from thefirst inspection package to the user. The first designation may beoverlaid on the second inspection image of the second inspectionpackage. Overlaying the first designation on the second inspection imagemay provide a cue, guide, or indication for the user, which may assistthe user with the inspection.

Method 1000 can include a step 1030 of determining a first property ofthe object 100 based on the first inspection image 400 of the object,the one or more properties maps of the object, and the firstdesignation. As mentioned in reference to step 820 of method 800, aproperty of the object 100 can be determined based on guide image data,which includes a properties map and the designation. Method 1000 caninclude a step 1040 of determining a second property of the object 100based on the second inspection image 400 of the object, the one or moreproperties maps of the object, and the second designation. As brieflymentioned, algorithms for determining the properties map may change overtime. As such, it may be beneficial for the first property of the object100 and the second property of the object 100 to be both determinedbased on the same properties map. However, in other examples, it may bebeneficial for the first property of the object 100 and the secondproperty of the object 100 to be based on different properties maps.

Method 1000 can include a step 1050 of comparing the first property withthe second property. Comparing the first property with the secondproperty may include determining a difference between a feature on theobject 100. For example, comparing the first property with the secondproperty may include determining a growth of the feature on the object100. As mentioned, the feature could be damage to the object 100 thatmay be visible to the user in the inspection image 400. In someexamples, the feature could be a crack on the object 100, a chip on theobject 100, or a spalling location of a coating on the object 100. Assuch, comparing the first property with the second property may includedetermining a growth or change of the crack, chip, or spalling of thecoating.

In some examples, the first inspection image 400 defines a first capturedate, and the second inspection image 400 defines a second capture date.The second capture date may be after the first capture date. Forexample, the second capture date may be at least one month later thanthe first capture date, such as at least six months later, such as atleast one year later, such as at least three years later, and up totwenty years later than the first capture date, such as up to fifteenyears later, such as up to ten years later, such as up to five yearslater. In at least one example, the first inspection image 400 defines afirst use-related metric, such as number of hours or number of cycles,and the second inspection image 400 defines a second use-related metric.The second use-related metric may be greater than the first use-relatedmetric. For example, the second use-related metric may be at least tencycles greater than the first use-related metric, such as at leasttwenty cycles greater, such as at least fifty cycles greater, such as atleast one-hundred cycles greater, such as at least one-thousand cyclesgreater, and up to one-thousand cycles greater, such as up tofive-hundred cycles greater, such as up to one-hundred cycles greater.

In some examples, it may be beneficial to determine the growth or changeof a crack, chip, or spalling of a coating periodically. For example,when the object 100 is a rotor blade for a gas turbine engine, it may bebeneficial to compare a property, such as a length of a crack on therotor blade, when the engine that the rotor blade is installed on isscheduled for maintenance or an inspection.

In some examples, determining the first property of the object 100comprises determining the first property of the object 100 proximate tothe second capture date. For example, the first property of the object100 may be determined on a date, a “determination date”, that is closerto the second capture date than the first capture date. As mentioned, itmay be beneficial to redetermine a property of the object 100 based on anewer algorithm so that an apples-to-apples comparison can be made, whenan older algorithm was previously used to determine the property of theobject. Therefore, the first property of the object 100 may have adetermination date that is closer to the second capture date than thefirst capture date.

Referring to FIG. 21 , a block diagram of a computing system 2000 thatcan be used to implement methods and systems of the present disclosureis provided in accordance with an exemplary embodiment of the presentdisclosure. Computing system 2000 may be used to implement an inspectionsystem 3000 (FIG. 22 ), as will be described herein. It will beappreciated, however, that computing system 2000 is one example of asuitable computing system for implementing the inspection system andother computing elements described herein.

As shown, the computing system 2000 can include one or more computingdevices 2005. The one or more computing devices 2005 can include one ormore processors 2015 and one or more memory devices 2020. The one ormore processors 2015 can include any suitable processing device, such asa microprocessor, microcontroller, integrated circuit, logic device, orother suitable processing device. The one or more memory devices 2020can include one or more computer-readable media, including, but notlimited to, non-transitory computer-readable media, RAM, ROM, harddrives, flash drives, or other memory devices. The one or more memorydevices 2020 can include remote storage or internet storage, such ascloud storage.

The one or more memory devices 2020 can store information accessible bythe one or more processors 2015, including computer-readableinstructions 2025 that can be executed by the one or more processors2015. The computer-readable instructions 2025 can be any set ofinstructions that when executed by the one or more processors 2015,cause the one or more processors 2015 to perform operations. Thecomputer-readable instructions 2025 can be software written in anysuitable programming language or can be implemented in hardware. In someembodiments, the computer-readable instructions 2025 can be executed bythe one or more processors 2015 to cause the one or more processors 2015to perform operations, such as the operations for controlling aninspection system, and/or any other operations or functions of the oneor more computing devices 2005.

The memory devices 2020 can further store data 2030 that can be accessedby the processors 2015. For example, the data 2030 can include pixelproperties 252, guide image pixel array 250, transform data, etc., asdescribed herein. The data 2030 can include one or more tables, such astable 260, functions, algorithms, such as algorithm 259, images, such assimplified images 210 and 410, equations, etc. according to exampleembodiments of the present disclosure.

The one or more computing devices 2005 can also include a communicationinterface 2040 used to communicate, for example, with the othercomponents of the system. The communication interface 2040 can includeany suitable components for interfacing with one or more networks,including for example, transmitters, receivers, ports, controllers,antennas, or other suitable components.

The technology discussed herein makes reference to computer-basedsystems and actions taken by and information sent to and fromcomputer-based systems. One of ordinary skill in the art will recognizethat the inherent flexibility of computer-based systems allows for agreat variety of possible configurations, combinations, and divisions oftasks and functionality between and among components. For instance,methods and processes discussed herein can be implemented using a singlecomputing device or multiple computing devices working in combination.Databases, memory, instructions, and applications can be implemented ona single system or distributed across multiple systems. Distributedcomponents can operate sequentially or in parallel.

Referring to FIG. 22 , a block diagram of an inspection system 3000 isprovided in accordance with an exemplary embodiment of the presentdisclosure. The inspection system 3000 can include one or more computingdevices 2005, one or more peripheral devices 3010, and one or morevisual image recording devices 3020. As mentioned, a peripheral device3010 can be a device that a user could use to designate one or moreinspection image pixels 451 of an inspection image 400. The peripheraldevice 3010 could be a touch screen, a stylus, a mouse, a rollerball,etc. Also, as mentioned, a peripheral device 3010 may be a device thatcan display an image to a user. For example, the peripheral device 3010may be a computer monitor and may be a touch screen monitor.

As mentioned, a visual image recording device 3020 may be a camera, suchas a borescope camera or an endoscope camera. The visual image recordingdevice 3020 may be a monocular camera or a binocular camera. The use ofa monocular camera may be beneficial to reduce costs and complexity, andincrease reliability and accuracy, as compared to a binocular camera.Also, even though not depicted, the visual image recording device 3020may be a component of an inspection tool assembly. The inspection toolassembly can assist the operator with taking photographs at a desiredorientation.

Even though the disclosure has periodically referred to a gas turbineengine, it should be understood that the methods described can be usedto inspect objects on other devices such as wind turbines, automobiles,hydrogen engines, steam engines, etc.

This written description uses examples to disclose the presentdisclosure, including the best mode, and also to enable any personskilled in the art to practice the disclosure, including making andusing any devices or systems and performing any incorporated methods.The patentable scope of the disclosure is defined by the claims, and mayinclude other examples that occur to those skilled in the art. Suchother examples are intended to be within the scope of the claims if theyinclude structural elements that do not differ from the literal languageof the claims, or if they include equivalent structural elements withinsubstantial differences from the literal languages of the claims.

Further aspects are provided by the subject matter of the followingclauses:

1. A method for inspecting an object, the method comprising receiving ordetermining inspection image data, the inspection image data includingan inspection image pixel array with at least one inspection image pixelin the inspection image pixel array having a pixel property associatedtherewith, receiving via a processor a user input associated with acontinuous segment of inspection image pixels in the inspection imagepixel array, and determining a property of the object based on the pixelproperties associated with the continuous segment of inspection imagepixels in the inspection image pixel array.

2. The method of any preceding clause, wherein receiving the user inputcomprises receiving the user input through a peripheral device that isoperably coupled to the processor.

3. The method of any preceding clause, wherein the continuous segment ofinspection image pixels is a linear segment of inspection image pixels,and wherein determining the property of the object comprises determininga length.

4. The method of any preceding clause, wherein the pixel propertycomprises a distance measurement.

5. The method of any preceding clause, wherein the continuous segment ofinspection image pixels is a closed segment of inspection image pixels,and wherein determining the property of the object comprises determiningan area of the closed segment.

6. The method of any preceding clause, wherein the continuous segment ofinspection image pixels is a closed segment of inspection image pixels,and wherein determining the property of the object comprises determiningthe property of the object based on the pixel properties associated withthe closed segment of inspection image pixels in the inspection imagepixel array and further based on a pixel property associated with aninspection image pixel that is encompassed by the closed segment ofinspection image pixels in the inspection image pixel array.

7. The method of any preceding clause, wherein the pixel propertycomprises an area measurement.

8. The method of any preceding clause, comprising displaying aninspection image and the inspection image pixel array to a user.

9. The method of any preceding clause, wherein determining the propertyof the object based on the pixel properties associated with thecontinuous segment of inspection image pixels in the inspection imagepixel array comprises determining a plurality of properties of theobject based on the pixel properties associated with the continuoussegment of inspection image pixels in the inspection image pixel array.

10. The method of any preceding clause, wherein receiving the user inputfurther comprises receiving a line drawn by a user and associating theline with the continuous segment of inspection image pixels in theinspection image pixel array.

11. An inspection system, comprising a visual image recording deviceconfigured to generate an inspection image of an object, and a computingsystem comprising a processor, the computing system configured toreceive or determine inspection image data, the inspection image dataincluding an inspection image pixel array with at least one inspectionimage pixel in the inspection image pixel array having a pixel propertyassociated therewith, receive a user input via the processor, the userinput being associated with a continuous segment of inspection imagepixels in the inspection image pixel array, and determine a property ofthe object based on the pixel properties associated with the continuoussegment of inspection image pixels in the inspection image pixel array.

12. The inspection system of any preceding clause, wherein thecontinuous segment of inspection image pixels is a linear segment ofinspection image pixels, and wherein the property of the object is alength measurement.

13. The inspection system of any preceding clause, wherein the pixelproperty comprises a distance measurement.

14. The inspection system of any preceding clause, wherein thecontinuous segment of inspection image pixels is a closed segment ofinspection image pixels, and wherein the property of the object is anarea measurement.

15. The inspection system of any preceding clause, wherein thecontinuous segment of inspection image pixels is a closed segment ofinspection image pixels, and wherein the property of the object isdetermined based on the pixel properties associated with the closedsegment of inspection image pixels in the inspection image pixel arrayand a pixel property associated with a pixel that is encompassed by theclosed segment of inspection image pixels in the inspection image pixelarray.

16. The inspection system of any preceding clause, wherein the pixelproperty comprises an area measurement.

17. The inspection system of any preceding clause, wherein theinspection system comprises a peripheral device configured to displaythe inspection image and the inspection image pixel array to a user.

18. The inspection system of any preceding clause, wherein eachinspection image pixel is a subdivision of an inspection image.

19. The inspection system of any preceding clause, wherein the userinput is a line drawn by a user and the computing system is configuredto associate the line with the continuous segment of inspection imagepixels in the inspection image pixel array.

20. The inspection system of any preceding clause, wherein theinspection image data comprises a simplified image of a 3D model of theobject.

We claim:
 1. A method for inspecting an object, the method comprising:receiving or determining inspection image data, the inspection imagedata including an inspection image pixel array with at least oneinspection image pixel in the inspection image pixel array having apixel property associated therewith; receiving via a processor a userinput associated with a continuous segment of inspection image pixels inthe inspection image pixel array; and determining a property of theobject based on the pixel properties associated with the continuoussegment of inspection image pixels in the inspection image pixel array.2. The method of claim 1, wherein receiving the user input comprisesreceiving the user input through a peripheral device that is operablycoupled to the processor.
 3. The method of claim 1, wherein thecontinuous segment of inspection image pixels is a linear segment ofinspection image pixels, and wherein determining the property of theobject comprises determining a length.
 4. The method of claim 3, whereinthe pixel property comprises a distance measurement.
 5. The method ofclaim 1, wherein the continuous segment of inspection image pixels is aclosed segment of inspection image pixels, and wherein determining theproperty of the object comprises determining an area of the closedsegment.
 6. The method of claim 1, wherein the continuous segment ofinspection image pixels is a closed segment of inspection image pixels,and wherein determining the property of the object comprises determiningthe property of the object based on the pixel properties associated withthe closed segment of inspection image pixels in the inspection imagepixel array and further based on a pixel property associated with aninspection image pixel that is encompassed by the closed segment ofinspection image pixels in the inspection image pixel array.
 7. Themethod of claim 6, wherein the pixel property comprises an areameasurement.
 8. The method of claim 1, comprising displaying aninspection image and the inspection image pixel array to a user.
 9. Themethod of claim 1, wherein determining the property of the object basedon the pixel properties associated with the continuous segment ofinspection image pixels in the inspection image pixel array comprisesdetermining a plurality of properties of the object based on the pixelproperties associated with the continuous segment of inspection imagepixels in the inspection image pixel array.
 10. The method of claim 1,wherein receiving the user input further comprises receiving a linedrawn by a user and associating the line with the continuous segment ofinspection image pixels in the inspection image pixel array.
 11. Aninspection system, comprising: a visual image recording deviceconfigured to generate an inspection image of an object; and a computingsystem comprising a processor, the computing system configured to:receive or determine inspection image data, the inspection image dataincluding an inspection image pixel array with at least one inspectionimage pixel in the inspection image pixel array having a pixel propertyassociated therewith; receive a user input via the processor, the userinput being associated with a continuous segment of inspection imagepixels in the inspection image pixel array; and determine a property ofthe object based on the pixel properties associated with the continuoussegment of inspection image pixels in the inspection image pixel array.12. The inspection system of claim 11, wherein the continuous segment ofinspection image pixels is a linear segment of inspection image pixels,and wherein the property of the object is a length measurement.
 13. Theinspection system of claim 12, wherein the pixel property comprises adistance measurement.
 14. The inspection system of claim 11, wherein thecontinuous segment of inspection image pixels is a closed segment ofinspection image pixels, and wherein the property of the object is anarea measurement.
 15. The inspection system of claim 11, wherein thecontinuous segment of inspection image pixels is a closed segment ofinspection image pixels, and wherein the property of the object isdetermined based on the pixel properties associated with the closedsegment of inspection image pixels in the inspection image pixel arrayand a pixel property associated with a pixel that is encompassed by theclosed segment of inspection image pixels in the inspection image pixelarray.
 16. The inspection system of claim 15, wherein the pixel propertycomprises an area measurement.
 17. The inspection system of claim 11,wherein the inspection system comprises a peripheral device configuredto display the inspection image and the inspection image pixel array toa user.
 18. The inspection system of claim 11, wherein each inspectionimage pixel is a subdivision of an inspection image.
 19. The inspectionsystem of claim 11, wherein the user input is a line drawn by a user andthe computing system is configured to associate the line with thecontinuous segment of inspection image pixels in the inspection imagepixel array.
 20. The inspection system of claim 11, wherein theinspection image data comprises a simplified image of a 3D model of theobject.